def __init__(
self,
num_channels: int,
num_filters: int,
filter_size: int,
stride: int = 1,
groups: int = 1,
is_vd_mode: bool = False,
act: str = None,
name: str = None,
):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=True)
self._conv = Conv2d(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
self._batch_norm = BatchNorm(
num_filters,
act=act,
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def __init__(self, name: str):
super(InceptionA, self).__init__()
self._pool = AvgPool2d(kernel_size=3, stride=1, padding=1)
self._conv1 = ConvBNLayer(384, 96, 1, act="relu", name="inception_a" + name + "_1x1")
self._conv2 = ConvBNLayer(384, 96, 1, act="relu", name="inception_a" + name + "_1x1_2")
self._conv3_1 = ConvBNLayer(384, 64, 1, act="relu", name="inception_a" + name + "_3x3_reduce")
self._conv3_2 = ConvBNLayer(64, 96, 3, padding=1, act="relu", name="inception_a" + name + "_3x3")
self._conv4_1 = ConvBNLayer(384, 64, 1, act="relu", name="inception_a" + name + "_3x3_2_reduce")
self._conv4_2 = ConvBNLayer(64, 96, 3, padding=1, act="relu", name="inception_a" + name + "_3x3_2")
self._conv4_3 = ConvBNLayer(96, 96, 3, padding=1, act="relu", name="inception_a" + name + "_3x3_3")
def __init__(self, name: str = None):
super(InceptionB, self).__init__()
self._pool = AvgPool2d(kernel_size=3, stride=1, padding=1)
self._conv1 = ConvBNLayer(1024, 128, 1, act="relu", name="inception_b" + name + "_1x1")
self._conv2 = ConvBNLayer(1024, 384, 1, act="relu", name="inception_b" + name + "_1x1_2")
self._conv3_1 = ConvBNLayer(1024, 192, 1, act="relu", name="inception_b" + name + "_1x7_reduce")
self._conv3_2 = ConvBNLayer(192, 224, (1, 7), padding=(0, 3), act="relu", name="inception_b" + name + "_1x7")
self._conv3_3 = ConvBNLayer(224, 256, (7, 1), padding=(3, 0), act="relu", name="inception_b" + name + "_7x1")
self._conv4_1 = ConvBNLayer(1024, 192, 1, act="relu", name="inception_b" + name + "_7x1_2_reduce")
self._conv4_2 = ConvBNLayer(192, 192, (1, 7), padding=(0, 3), act="relu", name="inception_b" + name + "_1x7_2")
self._conv4_3 = ConvBNLayer(192, 224, (7, 1), padding=(3, 0), act="relu", name="inception_b" + name + "_7x1_2")
self._conv4_4 = ConvBNLayer(224, 224, (1, 7), padding=(0, 3), act="relu", name="inception_b" + name + "_1x7_3")
self._conv4_5 = ConvBNLayer(224, 256, (7, 1), padding=(3, 0), act="relu", name="inception_b" + name + "_7x1_3")
def __init__(self, name: str = None):
super(InceptionC, self).__init__()
self._pool = AvgPool2d(kernel_size=3, stride=1, padding=1)
self._conv1 = ConvBNLayer(1536, 256, 1, act="relu", name="inception_c" + name + "_1x1")
self._conv2 = ConvBNLayer(1536, 256, 1, act="relu", name="inception_c" + name + "_1x1_2")
self._conv3_0 = ConvBNLayer(1536, 384, 1, act="relu", name="inception_c" + name + "_1x1_3")
self._conv3_1 = ConvBNLayer(384, 256, (1, 3), padding=(0, 1), act="relu", name="inception_c" + name + "_1x3")
self._conv3_2 = ConvBNLayer(384, 256, (3, 1), padding=(1, 0), act="relu", name="inception_c" + name + "_3x1")
self._conv4_0 = ConvBNLayer(1536, 384, 1, act="relu", name="inception_c" + name + "_1x1_4")
self._conv4_00 = ConvBNLayer(384, 448, (1, 3), padding=(0, 1), act="relu", name="inception_c" + name + "_1x3_2")
self._conv4_000 = ConvBNLayer(
448, 512, (3, 1), padding=(1, 0), act="relu", name="inception_c" + name + "_3x1_2")
self._conv4_1 = ConvBNLayer(512, 256, (1, 3), padding=(0, 1), act="relu", name="inception_c" + name + "_1x3_3")
self._conv4_2 = ConvBNLayer(512, 256, (3, 1), padding=(1, 0), act="relu", name="inception_c" + name + "_3x1_3")
def __init__(self, class_dim: int = 1000, load_checkpoint: str = None):
super(GoogleNet, self).__init__()
self._conv = ConvLayer(3, 64, 7, 2, name="conv1")
self._pool = MaxPool2d(kernel_size=3, stride=2)
self._conv_1 = ConvLayer(64, 64, 1, name="conv2_1x1")
self._conv_2 = ConvLayer(64, 192, 3, name="conv2_3x3")
self._ince3a = Inception(192,
192,
64,
96,
128,
16,
32,
32,
name="ince3a")
self._ince3b = Inception(256,
256,
128,
128,
192,
32,
96,
64,
name="ince3b")
self._ince4a = Inception(480,
480,
192,
96,
208,
16,
48,
64,
name="ince4a")
self._ince4b = Inception(512,
512,
160,
112,
224,
24,
64,
64,
name="ince4b")
self._ince4c = Inception(512,
512,
128,
128,
256,
24,
64,
64,
name="ince4c")
self._ince4d = Inception(512,
512,
112,
144,
288,
32,
64,
64,
name="ince4d")
self._ince4e = Inception(528,
528,
256,
160,
320,
32,
128,
128,
name="ince4e")
self._ince5a = Inception(832,
832,
256,
160,
320,
32,
128,
128,
name="ince5a")
self._ince5b = Inception(832,
832,
384,
192,
384,
48,
128,
128,
name="ince5b")
self._pool_5 = AvgPool2d(kernel_size=7, stride=7)
self._drop = Dropout(p=0.4, mode="downscale_in_infer")
self._fc_out = Linear(1024,
class_dim,
weight_attr=xavier(1024, 1, "out"),
bias_attr=ParamAttr(name="out_offset"))
if load_checkpoint is not None:
model_dict = paddle.load(load_checkpoint)[0]
self.set_dict(model_dict)
print("load custom checkpoint success")
else:
checkpoint = os.path.join(self.directory,
'googlenet_imagenet.pdparams')
if not os.path.exists(checkpoint):
os.system(
'wget https://paddlehub.bj.bcebos.com/dygraph/image_classification/googlenet_imagenet.pdparams -O'
+ checkpoint)
model_dict = paddle.load(checkpoint)[0]
self.set_dict(model_dict)
print("load pretrained checkpoint success")